Multi-dimensional Medical Image Segmentation with Partial Volume and Gradient Modelling

We present a new algorithm for the feature-space based segmentation of medical image volumes, based on a unified mathematical framework that incorporates both intensity and local gradient information. The algorithm addresses the problem of partial volume tissue estimation and is capable of using multiple image volumes, and the associated multi-dimensional image gradient, to increase tissue separability. Clustering is performed in the combined intensity and gradient histogram, followed by the use of Bayes theory to generate probability maps showing the most likely tissue volume fractions within each voxel, rather than a classification to a single tissue type. The approach also supports reconstruction of images from the estimates of volumetric voxel contents and the tissue model parameters. Evaluation of the algorithm comprised three stages. First, objective measurements of segmentation accuracy, and the increase in accuracy when local gradient information was included in the feature space, were produced using simulated magnetic resonance (MR) images of the normal brain. Second, application to clinical MR data was demonstrated using an exemplar medical problem, the measurement of cerebrospinal fluid (CSF) volume in 70 normal volunteers, through comparison to a "bronze-standard" consisting of previously published measurements. Third, the accuracy of the multi-dimensional approach was demonstrated by assessing the errors on reconstructed images produced from the segmentation result. We conclude that the inclusion of gradient information in the feature space can result in significant improvements in segmentation accuracy compared to the use of intensity information alone.